To address these limitations, the second group of methods take advantage of the power of CNNs and directly expands the dynamic range to recover pixels in poorly exposed regions. Several novel architectures have been proposed to utilize contextual information extracted by convolutional layers to improve HDR reconstruction results [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. For example, Marnerides et al. [1]} proposed a three-branch CNN to extract global, semi-local, and local features that exploit feature recovery at various scales, for reconstruction in all poorly exposed regions. However, their method outputs overly bright and smooth images as it over-enhances the extracted illumination features. Liu et al. [6]} presented an HDR framework that incorporates the domain knowledge of the LDR image formation pipeline. They trained three specialized networks to reverse the image formation steps of dynamic range clipping, non-linear mapping from a CRF, and quantization to reconstruct an HDR image. Chen et al. [9]} proposed a U-Net like network to solve the joint tasks of HDR reconstruction, denoising, and dequantization. Zhang et al. [10]} designed a three-stage cascade network to decompose the LDR image to learn the HDR content. Eilersten et al. [2]} proposed a U-Net like architecture to regress details in over-exposed regions with a fixed mask, and later combined the prediction with the input image for the final result. Santos et al. [3]} developed an autoencoder structure with a feature masking mechanism to also regress details in over-exposed regions, and then reconstruct the HDR image through combining with the input image for the end result. Namely, the feature masking mechanism by Santos et al. [3]} is based on a soft mask computed from the pixel intensity of the input image. Thus, dark pixels in the soft mask correspond to over-exposed pixels where the CNN focuses its learning upon, and the soft mask is continuously updated by the feature weights of each convolutional layer. However, both methods by Eilersten et al. [2]} and Santos et al. [3]} neglect HDR reconstruction for under-exposed regions, and is unable to improve HDR estimation for properly exposed regions, as it applies a mask that focuses the reconstruction on over-exposed regions. Furthermore, the methods by Eilersten et al. [2]} and Santos et al. [3]} has unreliable reconstruction in all poorly exposed regions due to the lack of prior to enforce consistency.
